The 1986 Space Shuttle Challenger explosion is an emblematic case study of how data visualization can play an essential role in decision-making

The explosion happened due to low temperatures that affected shuttle parts

Edward Tufte, a visualization expert, argues that the cause of this tragedy was an unreadable format of data given to decision-makers

Data visualization is an attempt to make data more easily digestible by rendering it in a visual context (e.g., charting, graphing, etc.)

We use data visualization to transform raw data into something compelling

Data visualization is at the intersection of art and science

Visual context provides insights on patterns, trends, and correlations that might be difficult to detect otherwise

It is a simple way to convey concepts and provide visual access to large amounts of complex data

Using Python is excellent as it has multiple graphing libraries with many valuable features

To provide valuable, interpretable, and relevant insights

To give a visual or graphical representation of data / concepts

To communicate ideas

To provide an accessible way to see and understand trends, outliers, and patterns in data

To try to confirm a hypothesis

Exploratory data analysis (EDA) is the process of reviewing new data to discover patterns, spot anomalies, test hypotheses, and check assumptions

It helps to create graphs without breaking the train of thought as you explore your data

Visualization is an iterative process and consists of a few steps:

• Analyze
• Manipulate
• Graph
• Repeat

Categorical data is non-numeric or qualitative

Insight: comparisons and proportions

Chart types: vertical bar, column bar, horizontal bar, pie, bullet charts, stacked bar, and tree maps

Univariate data consists of a single numeric variable

Insight: distributions, proportions, and frequencies

Chart types: histogram, density, box plots

• Is the data normally distributed?
• Are there any outliers?
• Do you notice any other patterns in the data?
• These are some of the steps for initial data exploration

Bivariate data consists of two (or more) numeric variables (i.e., weight and height)

Insight: relationships, correlation, proportions, and frequencies

Chart types: scatterplot, bubble, parallel, radar, bullet, and heat

Trend data includes a time-based data (i.e., years, months, days, hours, etc.)

Insight: trends, comparisons, and cycles

Chart types: line, area, bubble, vertical bar

Text data includes alphanumeric single words or phrases (keywords)

Insight: sentiment, comparisons, and frequency

Chart types: word cloud, histogram, stacked bar chart

Geospatial data includes qualitative or quantitative information about specific locations

Insight: locations, comparisons, and trends

Chart types: chloropleth filled map, point map, connection map, isopleth map

Simple text is used when there is just a number or two to share. Simple text can be a great way to communicate something like:

• 440 employees worked a total of 31,702 days, an average of 72.05 days per employee

Tables are helpful when communicating to a mixed audience or showing a few different units of measure

Bar charts are used to express larger variations in data and how individual data points relate to a whole, comparisons, and ranking

They express quantities through a bar’s length, using a common baseline (=zero)

Note: when the data has lengthy names, using a horizontal bar chart will make the data easier to read

Line charts are used to plot continuous data in some unit of time, such as days, months, quarters or years

They can also be used to show multiple series of data

A line graph can also represent a summary statistic, like the average and confidence level range or the point estimate of a forecast

Area charts are used to summarize relationships between datasets, how individual data points relate to a whole

The visual at the right shows the monthly trend of active operations

In chat, share your thoughts on how you think this visual could be improved

Heatmaps visualize data in tabular format, using colored cells to show the relative magnitude of the numbers

When using a heatmap, it is helpful to restrict the number of different color gradations

The visual at the right shows the busiest months ranked by the number of operations for each department

Scatterplots show the type of relationship between two numeric variables

Scatterplots are often used in scientific fields and are sometimes viewed as “complicated” to understand, but there are real-world uses as well

In chat, share your thoughts on what relationship this scatterplot represents